Prior work on domain adaptation has been done for several NLP tasks.
Some works apply instance weighting on statistical machine translation (SMT) [1]} or cross-language text classification [2]}. Cross-entropy based method is used to select out-domain sentences for training SMT [3]}. There are also attempts for RC, showing that the performance of RC models on small datasets can be improved by supervised transferring from a large dataset [4]}, [5]} using annotations from both domains. MultiQA [6]} strengthens the generalizability of RC model by training on samples from various datasets. Though some studies concentrate on the generalization of RC models and analyze their performance on multiple datasets [7]}, [8]}, they do not analyse the influential factors in detail. A parallel work for RC unsupervised domain adaptation [9]} utilizes a simple self-labeling for re-training, and it is evaluated on 3 small datasets containing thousands of samples.
